Figures
Lecture 9
Review: t-test()
- One-sample t-test: t.test(x = sample, alternative = "two.sided"
or “greater” or “less”)
- Two-sample t-test: t.test(x = sample1, y = sample2, alternative
= "two.sided" or “greater” or “less”, var.equal = TRUE or
FALSE)
- Paired t-test: t.test(x = sample1, y = sample2, alternative =
"two.sided" or “greater” or “less”, paired = TRUE, var.equal =
TRUE or FALSE)
Figures
- From previous lectures, we have found numeric results
- Today, we’ll learn to show results visually
- Scatterplot
- Boxplot
- Histogram
- Ggplot2
Scatterplot
plot(x = __, y = ___, main = “__”, xlab = “__”, ylab = “__”)
- Scatterplot of Y v.s. X
Example:
- From Beer's law, we expect a graph of optden vs carb to
form a line
- plot(x = Formaldehyde$carb, y = Formaldehyde$optden)
Review of
lecture 7
plot() R Documentation
type
what type of plot should be drawn. Possible
types are
● "p" for points,
● "l" for lines,
● "b" for both,
● "c" for the lines part alone of "b",
● "o" for both ‘overplotted’,
● "h" for ‘histogram’ like (or ‘high-
density’) vertical lines,
● "s" for stair steps,
● "S" for other steps, see ‘Details’ below,
● "n" for no plotting.
All other types give a warning or an error;
using, e.g., type = "punkte" being equivalent
to type = "p" for S compatibility. Note that
some methods, e.g.plot.factor, do not accept
this.
main
an overall title for the plot: see title.
sub
a sub title for the plot: see title.
xlab
a title for the x axis: see title.
ylab
a title for the y axis: see title.
asp aspect ratio, see plot.window.
Boxplots
boxplot(x = __, main = “__”, xlab = “__”, ylab = “__”)
- Boxplot X
Example:
- Sleep dataset
- boxplot(x = sleep$extra, main = "Boxplot of sleep changes",
ylab = "hours")
We can subset control and experimental group, creating 2 boxplots
boxplot() R documentation
formula
a formula, such as y ~ grp, where y is a numeric vector of data values to be split into groups according to the
grouping variable grp (usually a factor). Note that ~ g1 + g2 is equivalent to g1:g2.
data
a data.frame (or list) from which the variables in formula should be taken.
subset
an optional vector specifying a subset of observations to be used for plotting.
na.action
a function which indicates what should happen when the data contain NAs. The default is to ignore missing
values in either the response or the group.
xlab, ylab
x- and y-axis annotation, since R 3.6.0 with a non-empty default. Can be suppressed by ann=FALSE.
Histograms
hist(x = __, main = “__”, xlab = “__”, ylab = “__”)
- Histogram of X
Example:
- Sleep dataset
- hist(x = sleep$extra, main = "Histogram of sleep changes",
xlab = "hours")
We can subset control and experimental group, creating 2 boxplots
hist() R documentation
x
a vector of values for which the histogram is desired.
breaks
one of:
● a vector giving the breakpoints between histogram cells,
● a function to compute the vector of breakpoints,
● a single number giving the number of cells for the histogram,
● a character string naming an algorithm to compute the number of cells (see ‘Details’),
● a function to compute the number of cells.
In the last three cases the number is a suggestion only; as the breakpoints will be set to pretty
values, the number is limited to 1e6 (with a warning if it was larger). If breaks is a function, the x
vector is supplied to it as the only argument (and the number of breaks is only limited by the amount
of available memory).
...
Your Turn: create a figure
- Create a plot of your choosing using data of your
choosing
- Potential data: sleep, Formaldehyde,
ChickWeight, heart
Hint:
plot(x = __, y = ___, main = “__”, xlab = “__”, ylab = “__”)
boxplot(x = __, main = “__”, xlab = “__”, ylab = “__”)
hist(x = __, main = “__”, xlab = “__”, ylab = “__”)
ggplot2
- How to start:
install.packages("ggplot2") #installs this package to your R
library(ggplot2) #opens the package for use
- Have these two line exactly as shown at the top of your R
script
- Only need to run the first line once, whereas run the second
line whenever you open the script again
Ggplot2: ggplot()
- ggplot(dataset, aes(x = __, y = __)) creates a blank plot
- Aesthetics (aes) are features on your plot
- Use aes() argument to specify what your x and/or y
variables are
- We then add stuff to the plot by adding layers
- + geom_point() adds points (scatterplot)
Ggplot2: Formaldehyde scatterplot
- ggplot(dataset, aes(x = __, y = __)) + geom_point()
Example: optden (absorbance) vs carb (concentration) in
Formaldehyde dataset
ggplot(Formaldehyde, aes(x = carb, y = optden))
ggplot(Formaldehyde, aes(x = carb, y = optden)) + geom_point()
Ggplot2: scatterplot points
- ggplot(dataset, aes(x = __, y = __)) +
geom_point(col = “__”, size = __)
- Add arguments to geom_point() to specify features of
points
- col or color = the color of the points
- size = the size of the points
- If the last symbol on the previous line is a +, you can have
the next function on a new line
Example: Formaldehyde Absorbance vs Concentration
ggplot(Formaldehyde, aes(x = carb, y = optden)) +
geom_point(col = "purple", size = 3)
Ggplot2: scatter plot line
- ggplot(dataset, aes(x = __, y = __)) +
geom_point(col = “__”, size = __) +
geom_smooth(method="lm", col = "__")
- Add linear model (with 95% CI) to plot
Example:
ggplot(Formaldehyde, aes(x = carb, y = optden)) +
geom_point(col = "purple", size = 3) +
geom_smooth(method="lm", col = "red")
Ggplot2: variables
- plot(variable_containing_ggplot)
Example:
#Code getting kind of long, let's store our plot in a variable
g <- ggplot(Formaldehyde, aes(x = carb, y = optden)) +
geom_point(col = "purple", size = 3) +
geom_smooth(method="lm", col = "red")
plot(g)
Ggplot2: labels
+ labs(title="TITLE", subtitle="SUBTITLE", y="Y AXIS LABEL",
x="X AXIS LABEL", caption="CAPTION")
Example:
g + labs(title="Beer's Law", subtitle="This is a ggplot2 subtitle",
y="Absorbance", x="Concentration", caption="Caption here
under plot")
You won’t be tested on
this, but for fun I wanted to
show BMI vs cholesterol
stratified by smoking status
(from lecture 7)
Your Turn: create a ggplot2 scatterplot
- Feel free to use any dataset and any variables within
that dataset
Hint:
install.packages("ggplot2")
library(ggplot2)
ggplot(Formaldehyde, aes(x = carb, y = optden)) +
geom_point(col = "purple", size = 3) +
geom_smooth(method="lm", col = "red") +
labs(title="Beer's Law", subtitle="This is a ggplot2 subtitle", y="Absorbance",
x="Concentration", caption="Caption here under plot")
Ggplot2: Boxplot
- ggplot(dataset, aes(x = __, y = __)) creates a blank plot
- Aesthetics (aes) are features on your plot
- Use aes() argument to specify what your x and y
variables are
- We then add stuff to the plot by adding layers
- + geom_point() adds points (scatterplot)
- + geom_boxplot() adds boxplots
Ggplot2: Boxplot with both x and y axes
Use aes(col = ___) to specify what variable to color based on
Ggplot2: Boxplot more layers
- Layers from when we created a scatterplot can also be used for
boxplots
Example:
ggplot(sleep, aes(x = group, y = extra, color=group)) +
geom_boxplot() +
labs(title = "Sleep Changes in the Control and Experimental Group") +
geom_point(col = "black")
Ggplot2: Boxplot Color Palette
Your Turn: create a ggplot2 boxplot
- Feel free to use any dataset and any variables within
that dataset
Hint:
install.packages("ggplot2")
library(ggplot2)
#Remove color = __
ggplot(sleep, aes(x = group, y = extra, color=group)) +
geom_boxplot() +
labs(title = "Sleep Changes in the Control and Experimental Group") +
cale_colour_brewer(palette = "Pastel1")
Ggplot2: Histogram
- ggplot(dataset, aes(x = __, y = __)) creates a blank plot
- Aesthetics (aes) are features on your plot
- Use aes() argument to specify what your x and y
variables are
- We then add stuff to the plot by adding layers
- + geom_point() adds points (scatterplot)
- + geom_boxplot() adds boxplots
- + geom_histogram() creates histogram
Your Turn: create a ggplot2 histogram
- Feel free to use any dataset and any variables within
that dataset
Hint:
install.packages("ggplot2")
library(ggplot2)
ggplot(sleep, aes(x=extra)) + geom_histogram()
ggplot(sleep, aes(x=extra, color=group, fill = group)) +
geom_histogram(bins = 8)
Just for fun: you can add
all sorts of layers, but it’s
up to you (the human) to
make sure what you’re
doing is correct for your
research.
I put points on top of a
boxplot on top of a
histogram. R doesn’t care
and will do it.
Check out the posted cheatsheet for
more ggplot functions
Ggplot2: Use R Documentation!
https://drsimonj.svbtle.com/pretty-scatter-plots-with-ggplot2
https://economicsfromthetopdown.com/2019/07/30/making-beautiful-charts-using-r-ggplot/
https://timogrossenbacher.ch/2019/04/bivariate-maps-with-ggplot2-and-sf/
https://www.r-spatial.org/r/2018/10/25/ggplot2-sf-2.html
https://mode.com/blog/r-ggplot-extension-packages/
https://www.google.com/url?sa=i&url=https%3A%2F%2Fvodkhang.com%2Fdatavisual%2Flinkedin-network-analysis-with-ggplot2&psig=AOvVaw2tHQcywTMpKWeifDhMG-
B0&ust=1604103628695000&source=images&cd=vfe&ved=0CA0QjhxqFwoTCNDEyvGF2-wCFQAAAAAdAAAAABAt
Unlimited possibilities!
